The Dynamic Systems and Control group at UC San Diego integrates, at a fundamental level, system design, modeling, and control disciplines to obtain improved performance of the dynamic response of engineering systems using feedback. As such, the areas of research of the Dynamic Systems and Control group is a joint activity in the topics of systems integration, dynamic system modeling, feedback control design, and the fundamentals of systems theory as applied to linear and nonlinear dynamic systems, mechatronics, structural control, aerospace, and fluid-mechanical systems.
The framework of port-Hamiltonian systems is a modeling paradigm and aims for a structured modeling approach that, on the one hand, is close to physics and, on the other hand, is particularly useful when it comes to mathematical analysis, approximation, simulation, an
Abstract: In this work, we consider a novel inverse problem in mean-field games (MFG).
To transform our lives, robots need to interact with other agents in complex shared environments. For example, autonomous cars need to interact with pedestrians, human-driven cars, and other autonomous cars. Autonomous delivery drones need to navigate in the aerial space shared by other drones, or mobile robots in a warehouse must navigate in the factory space shared by robots and humans.
With increasing levels of penetration of renewable generation and distributed energy resources (DERs) across the bulk transmission and distribution systems, there is an increasing reliance on fast acting controls, protection, and communication to keep the grid operating reliably and securely.
Aerodynamic gliders, such as gliding animals or plant material, can exhibit a rich array of dynamical behavior. We discuss some recent efforts to reveal underlying physical mechanisms, using a hierarchy of mathematical models and experiments.
A group of autonomous agents can accomplish certain missions more effectively and efficiently than individuals working alone, which may explain why such collective behaviors are often observed in nature and increasingly implemented in robotic applications (such as environmental monitoring, search and rescue, and
Trust between humans and multi-agent robotic swarms may be analyzed using human preferences. These preferences are expressed by an individual as a sequence of ordered comparisons between pairs of swarm behaviors. An individual’s preference graph can be formed from this se- quence. In addition, swarm behaviors may be mapped to a feature vector space. We formulate a linear optimization problem to locate a trusted behavior in the feature space.
The presentation will focus on two distinct topics involving the application of learning techniques to analysis of dynamical systems. First: we present an algorithm and a tool for statistical model checking (SMC) of continuous state space Markov chains initialized to a prescribed set of states. This model checking problem requires maximization of probabilities of sets of executions over all choices of initial states. We observe that it can be formulated as an X- armed bandit problem, and therefore, can be solved using hierarchical optimistic optimization.
Teams of autonomous robots and spacecraft can enable new space exploration missions, enhance the state-of-the-art (e.g. shortening the data collection period), or change the mission risk posture by introducing redundancy. Present-day monolithic systems (e.g. single spacecraft or rover) could be replaced by a team of interconnected and coordinating assets, a multi-agent system. These teams can increase the science return by cooperatively exploring an area of interest (e.g. rovers and helicopters) or making distributed measurements from different vantage points (e.g. smallsats).
We introduce a framework for generating, organizing, and reasoning with computational knowledge. It is motivated by the observation that most problems in Computational Sciences and Engineering (CSE) can be described as that of completing (from data) a computational graph representing dependencies between functions and variables. Functions and variables may be known, unknown, or random. Data comes in the form of observations of distinct values of a finite number of subsets of the variables of the graph (satisfying its functional dependencies).
